EP2447717A1 - Schnelles Verfahren zur gezielten Zellenselektion (Zelllinienselektion) - Google Patents

Schnelles Verfahren zur gezielten Zellenselektion (Zelllinienselektion) Download PDF

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Publication number
EP2447717A1
EP2447717A1 EP10014005A EP10014005A EP2447717A1 EP 2447717 A1 EP2447717 A1 EP 2447717A1 EP 10014005 A EP10014005 A EP 10014005A EP 10014005 A EP10014005 A EP 10014005A EP 2447717 A1 EP2447717 A1 EP 2447717A1
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EP
European Patent Office
Prior art keywords
cell
sample
data
standard
performance data
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EP10014005A
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English (en)
French (fr)
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EP2447717A9 (de
EP2447717B1 (de
Inventor
Dietmar Lang
Elaine B Martin
Gary A Montague
Christopher J O'Malley
Tracy S Root
Carol M Trim
Jane F Povey
Christopher M Smales
Andrew J Racher
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Lonza Biologics PLC
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Lonza Biologics PLC
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Priority to ES10014005T priority Critical patent/ES2434737T3/es
Priority to EP10014005.2A priority patent/EP2447717B1/de
Priority to TW100135910A priority patent/TW201231661A/zh
Priority to ES11776115.5T priority patent/ES2535608T3/es
Priority to JP2013535308A priority patent/JP5746768B2/ja
Priority to EP11776115.5A priority patent/EP2633313B1/de
Priority to CA2814697A priority patent/CA2814697A1/en
Priority to PCT/EP2011/005407 priority patent/WO2012055554A1/en
Priority to CN201180052286.2A priority patent/CN103189744B/zh
Priority to KR1020137013441A priority patent/KR20140026335A/ko
Priority to SG2013022132A priority patent/SG189087A1/en
Priority to AU2011322921A priority patent/AU2011322921A1/en
Priority to US13/881,170 priority patent/US10139418B2/en
Publication of EP2447717A1 publication Critical patent/EP2447717A1/de
Publication of EP2447717A9 publication Critical patent/EP2447717A9/de
Priority to HK13109216.4A priority patent/HK1181849A1/zh
Application granted granted Critical
Publication of EP2447717B1 publication Critical patent/EP2447717B1/de
Priority to US15/462,208 priority patent/US20170242029A1/en
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • G01N33/6803—General methods of protein analysis not limited to specific proteins or families of proteins
    • G01N33/6848—Methods of protein analysis involving mass spectrometry
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12N—MICROORGANISMS OR ENZYMES; COMPOSITIONS THEREOF; PROPAGATING, PRESERVING, OR MAINTAINING MICROORGANISMS; MUTATION OR GENETIC ENGINEERING; CULTURE MEDIA
    • C12N5/00—Undifferentiated human, animal or plant cells, e.g. cell lines; Tissues; Cultivation or maintenance thereof; Culture media therefor
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/5005—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving human or animal cells
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
    • G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
    • G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
    • G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01J—ELECTRIC DISCHARGE TUBES OR DISCHARGE LAMPS
    • H01J49/00—Particle spectrometers or separator tubes
    • H01J49/0027—Methods for using particle spectrometers
    • H01J49/0031—Step by step routines describing the use of the apparatus
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01J—ELECTRIC DISCHARGE TUBES OR DISCHARGE LAMPS
    • H01J49/00—Particle spectrometers or separator tubes
    • H01J49/02—Details
    • H01J49/10—Ion sources; Ion guns
    • H01J49/16—Ion sources; Ion guns using surface ionisation, e.g. field-, thermionic- or photo-emission
    • H01J49/161—Ion sources; Ion guns using surface ionisation, e.g. field-, thermionic- or photo-emission using photoionisation, e.g. by laser
    • H01J49/164—Laser desorption/ionisation, e.g. matrix-assisted laser desorption/ionisation [MALDI]
    • H—ELECTRICITY
    • H01—ELECTRIC ELEMENTS
    • H01J—ELECTRIC DISCHARGE TUBES OR DISCHARGE LAMPS
    • H01J49/00—Particle spectrometers or separator tubes
    • H01J49/26—Mass spectrometers or separator tubes
    • H01J49/34—Dynamic spectrometers
    • H01J49/40—Time-of-flight spectrometers

Definitions

  • the present invention relates to a process for the prediction of cell culture performance data of sample cells, a process for the isolation of said cells and a device for the prediction of cell culture performance data of sample cells.
  • Adrichem et al. disclose investigations of protein patterns and mammalian cells in culture supernatants by matrix-assisted laser desorption/ionisation mass spectrometry (MALDI mass spectrometry).
  • Said MALDI mass spectrometry can be used for protein profiling by monitoring proteins which have either been excreted into the media or located in cell lysates.
  • the detectable mass range by MALDI mass spectrometry is from 16000 to several hundred thousands of Daltons.
  • the results obtained thereby are complementary with standard SDS-PAGE electrophoresis. Therefore, these methods can be used for e.g. monitoring a large scale cultivation of hybridoma cells expressing an antibody of the IgG type.
  • Zhang et al. demonstrate that a different technique ⁇ a combination of liquid chromatography followed by electrospray ionisation and tandem mass spectroscopy (LC-ESI MS/MS) for which it is necessary to digest the samples ⁇ is also useful to shed light on proteome profile differences.
  • LC-ESI MS/MS electrospray ionisation and tandem mass spectroscopy
  • Feng et al. disclose a rapid characterisation of high/low producer CHO cells using matrix-assisted laser desorption/ionisation time-of-light (MALDI-TOF).
  • MALDI-TOF matrix-assisted laser desorption/ionisation time-of-light
  • the process disclosed therein is able to distinguish between high and low producer cells when produced in the same culture at the same scale by applying two statistical methods, namely principle component analysis (PCA) and linear partial least squares (PLS), to analyse the MALDI-TOF spectra.
  • PCA principle component analysis
  • PLS linear partial least squares
  • the method according to Feng et al. allows distinguishing between productivity data from different cell lines producing a recombinant protein IFN-gamma at the same scale, i.e. grown at low scale. According to Feng et al. this approach could possibly be used to predict cell productivity.
  • the linear PLS used derives its usefulness from the ability to analyse data with many variables which is relevant to find cell
  • the prior art fails to teach a method which predicts the cell characteristics of an unknown cell at a later stage of up-scaling, in particular in large volume bioreactors, while these cells are still cultivated in a medium with a low volume.
  • the known statistical programmes of PCA and PLS produce data being insufficient to be used as a basis for an accurate and reliable prediction of cell characteristics at a later state of up-scaling.
  • a cell line at the early stage expressing a specific balance of proteins can exibit for instance a high productivity at this stage, but after up-scaling to bioreactor stage this productivity can be deteriorated due to different factors, which are inter alia shear forces, volume effects, different fermenter type or format, cultivation parameters, for example pH and gas-controlled cell density differences.
  • the technical problem underlying the present invention is to provide a method to overcome the above identified problems, in particular to provide a method for the prediction of the cell culture, in particular bioreactor, performance of a cell with unknown cell characteristics, in particular already at an early state of up scaling, being additionally time-saving and cost-reducing, but having a high prediction accuracy for said performance in a large volume scale cultivation and/or under high productivity conditions, in particular in a bioreactor scale.
  • the present invention provides a process for the prediction of cell culture performance data of at least one sample cell comprising the steps:
  • the present invention therefore provides an advantageous process for the accurate and reliable prediction of cell culture, preferably bioreactor, performance data of at least one sample cell with unknown cell culture, preferably bioreactor, performance data, which allows the time saving and cost-reducing prediction of particular cell culture, preferably bioreactor, performance data, such as the productivity of cells.
  • the present invention not only provides such an advantageous process, but also provides a cell prepared, and in particular isolated, by said method, wherein said cell is particularly characterised by desired cell culture, preferably bioreactor, performance data, such as a high productivity.
  • the present invention provides a device for the prediction of cell culture performance data capable of conducting the process of the present invention.
  • the present invention obviates the cultivation steps in a 24 well plate and in a shake flask.
  • performance data of at least one sample cell cultivated in low volume, for instance in a 96 well plate can be performed.
  • the PLS-DA allows the separation of very specific classes of observations on the basis of one variable, so that with the use of PLS-DA the problem of productivity classification for cell lines is overcome and their cell culture, preferably bioreactor, performance at different scale can be predicted.
  • the cell culture performance data are preferably bioreactor performance data.
  • bioreactor performance data is understand to mean data on the behaviour and the characteristics of a cell, when said cell is cultivated or reproduced under a large volume condition and/or high productivity conditions, in particular in a bioreactor.
  • the bioreactor performance data are preferably data on the individual and specific cell productivity for a specific cell product, e. g. protein, in particular antibody, antibody fragment or fused antibody, antibiotics, cultivation needs, growth or lifetime of said cell.
  • the cell product is a protein, in particular an antibody, peptide, proteoglycan, glycoprotein, carbohydrate, lipid, antibiotic or hormone.
  • standard cell means a cell with known cell culture performance data, in particular known characteristics and behaviour at a given scale, preferably at a large volume scale and/or under high productivity conditions, in particular at a bioreactor scale. These known characteristics were measured and analysed by methods according to the state of the art. For instance, the cell productivity can be determined by ELISA (enzyme-linked immuno sorbent assay).
  • sample cell relates to a cell having at least one unknown cell culture performance data, in particular cell characteristic.
  • cell characteristic Preferably, at least one characteristic of said sample cell is known.
  • the term "low volume of a medium” means that the medium preferably has a volume of preferably 1 ⁇ l to 100 l, preferably 1 ⁇ l to 90 l, preferably 1 ⁇ l to 80 l, preferably 1 ⁇ l to 70 l, preferably 1 ⁇ l to 60 l, preferably 1 ⁇ l to 50 l, preferably 1 ⁇ l to 40 l, preferably 1 ⁇ l to 30 l, preferably 1 ⁇ l to 20 l, preferably 1 ⁇ l to 10 l, preferably 1 ⁇ l to 5 l, preferably 1 ⁇ l to 4 l, preferably 1 ⁇ l to 3 l, preferably 1 ⁇ l to 2 l, preferably 1 ⁇ l to 1 I, preferably 1 ⁇ l to 0.5 l, preferably 1 ⁇ l to 0.4 l, preferably 1 ⁇ l to 0.3 l, preferably 1 ⁇ l to 0.2 l, preferably 1 ⁇ l to 0.1 I, preferably 1 ⁇ l to 100 l
  • the term "early stage of up-scaling" is understood as a point of time where the cells are cultivated in a low volume of a medium as defined above.
  • bioreactor means a container capable of containing cells for the production of at least one desired cell product, which preferably enables a high productivity in terms of the production speed and/or amount of said desired cell product.
  • the bioreactor is a device or system supporting a biologically active environment.
  • a bioreactor of the present invention is a container suitable for industrial and commercial production of said cell product of interest.
  • such a container is able to create cell culture conditions suitable for producing the cell product of interest with a high productivity.
  • the bioreactor contains a volume of medium of at least 10 l, at least 20 l, at least 50 l, at least 100 l, at least 200 l, at least 300 l, at least 400 l, at least 500 l, at least 600 l, at least 700 l, at least 800 l, at least 900 l, at least 1000 l, in particular at least 2000 l, at least 3000 l or at least 4000 l.
  • the present invention provides a method which enables a person skilled in the art to predict the characteristics of at least one sample cell, especially at least one cell line, by providing in a first step a) a probe of at least one sample cell cultivated in a medium having a low volume and low cell concentration therein, preferably from 10 4 to 10 8 , the cell culture performance data of a standard cell and the raw standard MS data thereof. Subsequently, in a second step the at least one sample cell is analysed by a mass spectrometry method. In a third step both the raw MS data of the at least one sample cell and the standard cell are treated by a MS signal processing method so as to obtain pre-treated MS profiles of the at least one sample cell and standard cell.
  • a fourth step the cell culture performance data from the standard cell and the pre-treated sample and standard MS profiles are subjected to a statistical method, namely PLS-DA.
  • This statistical program is employed to compare and evaluate the pre-treated MS profiles of the sample cell to the pre-treated MS profiles of a standard cell.
  • the concentration of the cells is from 10 4 to 10 8 , from 10 5 to 10 7 , in particular from 10 6 to 10 7 viable cells/ml culture medium after finishing the cultivation.
  • the present invention enables the prediction of the cell culture, preferably bioreactor, performance data, e.g. the productivity, of the at least one sample cell being cultivated at an early stage of up-scaling.
  • performance data e.g. the productivity
  • the method according to the present invention allows the rapid development of for instance highly productive cell lines and thereby reduces the costs of therapeutic protein manufacturing and speeds up the development of pharmaceuticals.
  • the present process provides a novel screening tool for identifying, preferably early in the product development cycle, i. e. in a phase where a culture medium with a volume of 1 to 999 ⁇ l is used, cells, preferably cell lines, that have the desired properties, for instance a high volumetric productivity, in particular in large-scale bioreactors, in particular in bioreactors containing culture medium in a volume above 10 litre.
  • the method according to the present invention improves the probability of finding a cell, especially cell line, with specific cell characteristics, for instance comprising a high product productivity, preferably at least 1 g/I, preferably at least 5 g/I and most preferably 10 g/l, early in development.
  • the method can also be used preferably for isolating new host cells, preferably cell lines, with improved properties for instance for therapeutic protein manufacturing, especially monoclonal antibody manufacturing.
  • the present process allows the identification of patterns in different product productivity levels as to predict the productivities at different scale therefrom in a fast and, if applicable, an automatic way.
  • the high probability of finding high producing cells has the potential to reduce the number of cells, preferably cell lines, that needed to be screened before one suitable for manufacturing is identified.
  • the materials required are reduced. Consequently, fewer resources will be required during the cell, preferably cell line, construction with concomitant generation of less waste materials.
  • Higher producing cells, preferably cell lines will reduce the number of bioreactor cultures required to supply the market requirements for the product.
  • the present process is able to reduce the raw material requirements, in particular costs, of the production process, especially water that is used both as a raw material and in cleaning and sanitisation of the equipment.
  • the bioreactor performance data is cell specific productivity, integral viable cell count or cell product concentration. In a particularly preferred embodiment the bioreactor performance data is the specific cell productivity.
  • sample cell and/or standard cell is selected from the group consisting of human cell lines, animal cell lines, plant cell lines, antibodies, cells from fungi, cells from bacteria, cells from yeast and stem cells.
  • both the sample cell and the standard cell is selected from the same cell type, especially cell line or strain.
  • the sample cell is a modified CHO-K1 cell line.
  • the standard cell is also a modified CHO-K1 cell line.
  • the MS analysis used in step b) is selected from the group consisting of MALDI-TOF, LC-ESI-MS (liquid chromatography electrospray ionisation mass spectrometry) and LC-ESI-MS/MS (liquid chromatography coupled to tandem mass spectrometry with electrospray ionization).
  • the MS analysis used in step b) is MALDI-TOF.
  • the sample cells subjected to MALDI-TOF need not to be digested by e.g. trypsin, but can be embedded into the matrix in intact form by a very simple preparation of the cells.
  • the MS analysis used in step b) is LC-ESI-MS.
  • LC-ESI-MS analysis provides a particular great discrimination between cell lines in terms of productivity, growth and other desirable characteristics.
  • the LC-ESI-MS analysis provides an extra dimension of information.
  • the raw standard MS data is obtained by MS analysis selected from the group consisting of MALDI-TOF, LC-ESI-MS and LC-ESI-MS/MS, preferably MALDI-TOF.
  • the ionisation used for MALDI-TOF MS or LC-ESI-MS is carried out in a negative or positive reflection mode or in a positive or negative linear mode being optimal according to instrument-specific parameters, for example being device-dependent, with or without mass suppression and pulsed ion extraction.
  • the following settings of the MALDI-TOF mass spectrometer instrument are used for the method according to the present invention:
  • the mass suppression during the MS analysis of MALDI-TOF and LC-ESI-MS is below 500 Da, preferably below 1000 Da and most preferred below 1500 Da.
  • the mass suppression during the MS analysis of MALDI-TOF and LC-ESI-MS is above 500 Da, preferably above 1000 Da and most preferred above 1500 Da.
  • the detected range during the MS analysis of MALDI-TOF and LC-ESI-MS is 200 to 100000 Da, preferably 500 to 50000 Da, preferably 1000 to 100000 Da, 1000 to 18000 Da, preferably 500 to 10000 Da and most preferably 200 to 8000 Da.
  • the at least one sample cell is washed, preferably with a buffer solution, before being subjected to the MS analysis of MALDI-TOF.
  • the at least one sample cell is washed either with phosphate buffered saline (PBS) alone or followed by an aqueous sucrose solution wash step, in particular with 0.2 to 0.7 M, preferably 0.3 to 0.5 M, preferably 0.35 M sucrose before being subjected to the MS analysis of MALDI-TOF.
  • PBS phosphate buffered saline
  • the matrix used for the MALDI-TOF analysis is sinapinic acid (SA).
  • SA sinapinic acid
  • the use of sinapinic acid (SA) as a matrix for the MALDI-TOF MS analysis provides advantageous spectra with a particular wide range of peaks, preferably up to 70 kDa, and being particularly well resolved.
  • 2,5-dihydroxybenzoic acid (DHB) can also be used as a matrix.
  • the probe of the at least one sample cell subjected in step b) comprises not more than 1 x 10 6 cells, preferably 0.015 x 10 6 to 0.0625 x 10 6 cells, in particular 0.03 x 10 6 cells.
  • the MS profiles are taken after 1 to 5 hours, preferably 1 to 4 hours, in particular 1 to 3 hours of acclimatisation at low temperature, for instance at 0 to 10 °C, preferably at 2 to 8°C, in particular at 4°C, resulting in the best reproducibly and a lower signal to noise-containing mass spectra.
  • sample cells are subjected to the MS analysis at a specific time of growth.
  • the preferred sampling times are mid and/or end log phase of the cell growth.
  • the raw sample MS data obtained in step b) and/or the raw standard MS data provided in step a) are signal processed by an operation selected from the group consisting of baseline correction, normalisation, alignment, filtering and cropping.
  • MS data profiles typically exhibit a varied baseline due to issues such as chemical noise in the MALDI matrix and ion overloading. This is undesirable when using data analysis techniques to compare MS profiles as their utilised distance matrix to measure the similarity between profiles. Therefore, in a preferred embodiment of the present invention the raw sample and/or standard MS data are signal processed by the operation of baseline correction.
  • the raw sample and/or standard MS data are signal processed by the operation of normalisation.
  • Peak alignment is used to correct variation between the observed M/Z value and the true time of light. These errors usually occur as a result of calibration errors and can be observed as a systematic shift between peaks. Therefore, in a preferred embodiment of the present invention the raw sample and/or standard MS data are signal processed by the operation of peak alignment.
  • Filtering of the MS profiles is carried out by smoothing the signal preferably by a Savitzky-Golay filter. Therefore, in a preferred embodiment of the present invention the raw sample and/or standard MS data are signal processed by the operation of filtering.
  • Cropping the MS profiles is performed to remove parts of the signal containing little or no information.
  • the range of 0 to 500 m/z units is removed from the MS spectra. Therefore, in a preferred embodiment of the present invention the raw sample and/or standard MS data are signal processed by the operation of cropping.
  • the probe of the at least one sample cell is re-sampled.
  • This specific preferred process step allows up-sampling and down-sampling of the original signal, whilst preserving the information contained within the spectra, i.e. altering the amounts channels of different data points measured.
  • re-sampling is utilised in situations where the original high resolution MS signal would be considered impractical to work with due to computational constraints such as lack of computer memory.
  • Re-sampling can also be used to create a consistent m/z range, which facilitates lining up multiple spectra.
  • the raw standard and sample MS data are visually analysed.
  • the pre-treated standard and sample MS profiles obtained in step c) are optically analysed.
  • the sample cell with the predicted cell culture, preferably bioreactor, performance data evaluated in step d) is cultivated in a cell culture, preferably bioreactor, so as to verify its cell culture, preferably bioreactor, performance data.
  • the raw sample MS profiles obtained in step b) and the verified bioreactor performance data of the sample cell are used in step a) as standard MS data and bioreactor performance data from the standard cell.
  • the PLS-DA model according to step (d) requires two different sets of information, namely the x-block and the y-block.
  • the x-block contains the information from within the pre-treated sample MS data generated at the 96 DWP (deep well plate) stage of the process.
  • each pre-treated sample MS data is treated as a sample, with the signal intensities recorded over a specific range of m/z values being treated as the variables.
  • the y-block contains information assigning each of the pre-treated standard MS data to a class variable.
  • the y-block contains information preferably relating to specific measures of productivity of a cell line at the bioreactor scale i.e. product concentration, specific productivity or integral of viable cell count.
  • a PLS mapping of the original variables into the latent variable space is performed. This has the effect of reducing the dimensionality of the problem, whilst describing as much of the variability in the original data as possible.
  • the PLS-DA algorithm utilises the information in the y-block to fit the linear discrimination boundary that best separates the x-block data based on the class information stored in the y-block. If there are only two classes described in the y-block, a single discrimination boundary is sufficient. In cases where three or more classes are present, the within class samples should be compared to the out of class samples for each available class.
  • the present invention provides a process for the preparation, in particular isolation, of a cell with desired cell culture, preferably bioreactor, bioreactor performance data, wherein the process for the prediction of cell culture, preferably bioreactor, bioreactor performance data of at least one sample cell is performed and the at least one desired cell is prepared, preferably isolated.
  • the present invention provides a cell obtained, in particular isolated, by a process according to the present invention.
  • the cell isolated is characterised by a protein productivity of at least 1 g/l, preferably at least 5 g/I, preferably at least 6, 7, 8, 9 or preferably at least 10 g/I.
  • the present invention solves its underlying problem also by a device for the prediction of cell culture, preferably bioreactor, performance data of at least one sample cell comprising: a) a means adapted for subjecting a probe of the sample cell to a MS (mass spectrometric) analysis to obtain a raw sample MS data thereof, (b) a means adapted for subjecting a raw standard and the raw sample MS data to at least one first MS signal processing method to obtain pre-treated standard and sample MS profiles and (c) a means adapted for subjecting cell culture, preferably bioreactor, bioreactor performance data from a standard cell and the pre-treated sample and standard MS profiles to a second MS signal processing method including a PLS-DA (partial least square discriminant analysis) based comparative evaluation so as to predict the bioreactor performance data of the sample cell.
  • a device for the prediction of cell culture, preferably bioreactor, performance data of at least one sample cell comprising: a) a means adapted for subjecting a probe of the sample cell to a
  • a GS expression vector (Lonza) containing gene-optimised heavy and light chain genes for the expression of a model mouse-human chimeric lgG4 or lgG1 antibody ( Kalwy et al., Mol. Biotechnol 2006, 34, 151-156 ) was used to generate recombinant, antibody expressing GS-CHO cell lines.
  • the vector was introduced into the host cell line, CHOK1SV (a derivative of CHO-K1; Lonza), using standard electroporation methods and the transfection mixture was distributed across eighty 96-well plates. Plates were incubated at 37°C in a humidified, 10% CO 2 in air atmosphere. The following day, fresh medium was added to the cell suspension in the plates.
  • MSX methionine sulphoxamine
  • Cell concentration of the cultures was determined using a Vi-CELLTM automated cell viability analyser (Beckman Coulter). Cultures were established in 125 mL shake-flasks with a target cell concentration of 2.0 x 10 5 viable cells/mL and a final volume of typically 30 mL. Cell lines were serially subcultured on a 4 day regime. Once acceptable cell concentrations at subculture were reached and any large fluctuations in viable cell concentration between subcultures had ceased, the assessment stages performed in suspension culture commenced. The 'fed-batch' assessment was performed after the cell lines were ranked following the first suspension evaluation ('batch'). For the fed-batch assessment, the cell concentration of the cultures was determined on days 7 and 14 using a Vi-CELLTM automated cell viability analyser.
  • a bolus addition of feed A was made on day 3 and bolus additions of feed B were made on days 8 and 11. Samples of culture supernatant were taken on different days for antibody concentration determination. Cell viability analysis could alternatively be done with MACSQuant® Analyzer.
  • cells were subsequently washed with 1 ml of 0.35 M sucrose and the supernatant removed after centrifuging as described above. At that point cell pellets could be stored (-80°C) for further handling in the future or immediately processed for MS analysis. In case of storage frozen cell pellets need to equilibrate to room temperature before used after thawing.
  • matrix buffer (40% acetonitrile, 60% 0.1% TFA) which results in a saturated solution.
  • the sinapinic acid solution was then placed in a sonicating water bath for 15 minutes before centrifugation at 17900 rcf (13000 rpm) for 5 minutes in an Eppendorf microfuge (model 5417c, rotor F-45-30-11 ).
  • Matrix solution 50 ⁇ l was then added to each sample and the cells re-suspended by manually pipetting the solution up and down. After resuspension the cells were placed at 4°C for up to several hours. On removal from 4°C, the cells were re-suspended by gently tapping the tube and then 1 ⁇ l of each sample was spotted onto a 384 MTP ground steel MALDI TOF plate (Bruker). Samples were allowed to air dry before the plate was put into the MALDI TOF machine (Bruker Ultraflex) and the samples analysed.
  • Sample collection A range of CHO cell lines were grown in 250 ml suspension cell culture flasks. Cells were counted using a Vi-CELLTM and the cell number required (1 x 10 6 to 0.015625 x 10 6 ) were pipetted into 1.5 ml Eppendorf tubes and centrifuged at 960 rcf in an Eppendorf microfuge (model 5417c, rotor F-45-30-11) for 5 mins and the supernatant removed. The pellets were stored at -80°C until used.
  • Acetone precipitation A 4:1 dilution of 100% ice cold acetone to sample was incubated for 1 h at -20°C. The diluted sample was then centrifuged at 8870.4g for 10 min, the supernatant removed and the pellet left to dry at air briefly (not more than 5 min).
  • Tryptic digest in solution The pellet was re-suspended in 50 ⁇ l of 8 M urea, 0.4 M ammonium bicarbonate (NH 4 HCO 3 ) by pipetting the sample up and down to initially dislodge the pellet followed by brief vortexing.
  • the sample was reduced chemically by adding 2.5 ⁇ l of 100 mM dithiothreitol (DTT) in 50 mM NH 4 HCO 3 for 1 h in a 37°C incubator.
  • DTT dithiothreitol
  • the sample was then alkylated by adding 5 ⁇ l of 100 mM iodoacetamide in 50 mM NH 4 HCO 3 for 15 min at RT in the dark.
  • the urea concentration was diluted to ⁇ 2 M by adding 192.5 ⁇ l of HPLC grade water followed by the addition of 10 ⁇ l of 0.25 ⁇ g/ul modified trypsin (Promega). Tryptic digestion was then left to proceed overnight in a 37°C incubator. The sample was then dried down using a Savant speed vac (SC110A) on a low setting and resuspended in 20 ⁇ l of 0.1% formic acid, centrifuged for 8870.4g for 1 min, the supernatant removed and any pellet resuspended and centrifuged again at 8870.4g for 1 min then pipetted into screw cap vials with inserts and frozen at -80°C until analysed by LC-ESI-MS.
  • SC110A Savant speed vac
  • the files produced by the LC-ESI-MS were then converted from the proprietary file format (Bruker or Waters) to a universal standard (mzXML) (i.e. using CompassExport) and the resulting files and data subjected to a binning procedure.
  • the binning approach which is standard for the analysis of this type of MS data, allows the comparison of multiple ESI-MS datasets from different (or the same) samples by aligning them and involves dividing the retention time (elution time from LC system) and m/z range (mass to charge ratio of ions as detected in ESI-MS) into equally spaced intervals, for example, using a retention time bin of 60 seconds and a mass to charge bin of 1 m/z unit per bin.
  • Table 1 Example HPLC gradient run. A 35 min gradient with a flow rate of 0.3 ⁇ l/min throughout the run using a multistep gradient as displayed below with buffer A comprising 0.1 % formic acid and buffer B comprising 80% acetonitrile (ACN) and 0.1 % formic acid. Time % of buffer B Flow rate ⁇ l/min ) 0 4 0.3 0 4 0.3 10 55 0.3 11 90 0.3 16 90 0.3 17 4 0.3 35 4 0.3
  • a software tool ⁇ run via a Windows interface - which allows the fast and across scale prediction of cell line productivity is used. It is compiled in MATLAB (release 2008b, reference) using the MATLAB Bioinformatics and Statistics toolboxes as well as the PLS_Toolbox (www.eigenvector.com).
  • the software application starts with the availability of MS profiles from sample and standard cell lines having been grown under different culture conditions and scales.
  • the signal processing tools have been applied to the MS profiles to extract unique MS data patterns indicative of different levels of product producing cell lines.
  • Re-sampling is utilised in situations where the original high resolution MS signal would be considered impractical to work with due to computational constraints such as lack of computer memory.
  • Re-sampling can also be used to create a consistent m/z range, which facilitates lining up multiple spectra. Care must be taken when re-sampling MS profiles so as not to set the number of re-sampled units too low. This will cause the signals to lose resolution and can result in a loss of features.
  • MS data profiles typically exhibit a varied baseline due to issues such as chemical noise in the MALDI matrix and ion overloading. This can be undesirable when using data analysis techniques to compare MS profiles as they utilise distance metrics to measure the similarity between profiles. It is therefore preferred to remove these effects prior to any form of comparative analysis of the signals. This is performed using the 'msbackadj' function in the MATLAB Bioinformatics Toolbox (http://tinyurl.com/msbackadj). Figures 2a and 2b show a selection of typical 96DWP spectra before and after application of baseline correction.
  • baseline correction should be used after down-sampling and prior to correcting the calibration, as the noise present will impact on the result.
  • Peak alignment is used to correct variation between the observed m/z value and true time of flight. These errors usually occur as a result of calibration errors and can be observed as a systematic shift between peaks. Correction of these inconsistencies can be performed using the 'msalign' function from the MATLAB Bioinformatics Toolbox (http://tinyurl.com/msalign).
  • One method to align spectra is to spike the samples with a substance with a known spectral profile, and align the samples based on this. However, in situations where the samples have not been spiked, samples can be aligned relative to reference spectra such as the mean profile.
  • a typical MS profile contains a mixture of both signal and noise. Smoothing of the signal by use of a Savitzky-Golay filter can help to reduce the impact of the noise component of the signal during subsequent processing.
  • Savitzky-Golay filters are typically applied to MS signals as they use high order polynomials to fit the curves. This results in greater preservation of the features in the signal, such as the peak heights. This process is performed using the 'mssgolay' function from the MATLAB Bioinformatics Toolbox (http://tinyurl.com/mssgolay).
  • Figures 4a and 4b show a selection of typical 96DWP spectra before and after application of Savitzky-Golay filtering.
  • Cropping of the MS profiles is performed to remove parts of the signal containing little or no information. It also allows the spectra to be divided into subsections. This enables specific regions of the MS profiles to be analysed rather than the whole spectra.
  • Figures 5a and 5b show a selection of typical 96DWP spectra before and after cropping of the signal in the range of from 0 to 500 m/z.
  • Baseline correction only accounts for the noise in the signal due to the MALDI matrix. It is preferred that the variation in the amplitudes of the signal be removed using normalisation.
  • Figures 8a and 8b show the effect of applying normalisation on the group of spectra. It clearly shows a reduction in the variation observed in PC1, the major source of variation.
  • FIGS 9a and 9b show the effect of removing the data points in the MS profile over the range from 0 to 500 m/z units, as the MALDI was set not to record intensities in this range. It is clear from the scores plot that no effect is observed by removing this data, as the scores plot is identical to that observed in Figures 8a and 8b .
  • PLSDA is an application of multivariate least squares modelling specifically formulated for predictive classification.
  • the developed MS fingerprinting approach utilised in the example employs the PLS_Toolbox implementation of PLSDA, published by Eigenvector Research, Inc. (EVRI) (www.eigenvector.com).
  • the x-block contains the information from within the spectral profiles generated at the 96DWP stage of the process. Each profile is treated as a sample, with the signal intensities recorded over a specific range of m/z values being treated as the variables.
  • the y-block contains information assigning each of the training samples to a class variable. In this example the y-block contains information relating to specific cell culture data of productivity of a cell line at the bioreactor scale, i.e. product concentration, specific productivity or integral of viable cell count.
  • a PLS mapping of the original variables into the latent variable space is performed. This has the effect of reducing the dimensionality of the problem, whilst describing as much of the variability in the original data as possible.
  • the PLSDA algorithm then utilises the information in the y-block to fit the linear discrimination boundary that best separates the x-block data based on the class information stored in the y-block. If there are only two classes described in the y-block, a single discrimination boundary is sufficient; in cases where three or more classes are present, the within class samples should be compared to the out of class samples for each available class.
  • Figure 10 shows the flowchart of operations required to build a PLSDA model using the graphical implementation of the algorithm found in the MATLAB PLS_Toolbox.
  • Example 7 MS profiles subjected to MALDI-TOF and their statistical modelling
  • Figures 17 and 18 show the results obtained for the BNCD model (data are baseline corrected, normalised and cropped with duplicate samples included).
  • the triangles show the training data for the "High” class (> 4000 mg/L)
  • stars show the training samples for the "Low” class ( ⁇ 3999 mg/L)
  • black dots without a cell line ID number represent samples for which the class data is unknown and dots with cell line ID number show the samples that fall above the classification boundary (upper grey dotted line).
  • a prediction model could be built including hundreds of MS data generated during the cell line generation process. Based on the model a list of the cell lines that were expected to produce different amount of MAb (> 4000mg/L; ⁇ 3999 mg/L) was collated. Table 2 highlights several cell lines which can be identified in figures 17 and 18 . The cell lines were grown with their titre values recorded to measure the performance of the prediction method (Table 2). Table 2 Predicted high/low producing cell lines vs.
  • Example 8 The results after the statistical modelling of MS profiles subjected to LC-ESI-MS
  • Figures 19a /b show the separation of three different recombinant CHO cell lines using a PLSDA analysis ( Figures 19a /b show LV1 ⁇ LV3) and Figure 20 shows the separation of seven different recombinant CHO cell lines after PLSDA analysis when the samples are grouped into ⁇ 2 g/L and >2 g/L.
  • the data shows two samples of each recombinant cell line group and that the PLSDA algorithm is capable of discriminating between cell lines belonging to different groups.
  • the approach is suitable for fingerprinting recombinant cell lines on the basis of desirable (e.g. productivity) characteristics.
  • Table 3 shows the product concentration of the CHO cell lines 2, 42, 52, 75, 106, 144 and 164 cultivated in a 24 well plate, batch, fed batch and bioreactor. Especially, the product concentration at bioreactor scale was predicted correctly by the PLS-DA analysis using LC-ESI-MS data ( Figure 20 ).
  • Table 3 CHO Cell line Product conc. 24 well plate (mg/L) Product conc. Batch (mg/L) Product conc. Fed batch (mg/L) Product conc.
  • bioreactor scale (mg/L) Grouping as shown in PLS-DA analysis of LC-ESI-MS data 42 230 538 2404 3220.00 >2 g/L 52 31.5 31.5 101 24.00 ⁇ 2 g/L 2 236 480 1680.5 2594.00 >2 g/L 144 175 391 1592 1816.00 ⁇ 2 g/L 75 241 606 1001 1826.00 ⁇ 2 g/L 106 221 766 969.5 2325.00 >2 g/L 164 202 534 881.5 2307.00 >2 g/L

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WO2020078747A1 (en) * 2018-10-15 2020-04-23 Sartorius Stedim Data Analytics Ab Multivariate approach for cell selection
CN112100924A (zh) * 2020-09-17 2020-12-18 云南电力技术有限责任公司 一种基于极限学习机模型的气体浓度的预测方法及装置
CN112102898A (zh) * 2020-09-22 2020-12-18 安徽大学 一种醋醅固态发酵过程谱图模态辨识方法及***
CN112102898B (zh) * 2020-09-22 2022-09-23 安徽大学 一种醋醅固态发酵过程谱图模态辨识方法及***

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